A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions.

In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality. This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired. The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns. By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.

A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections.

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and generated a "false color map" (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor's proliferation fraction directly on routinely H&E-stained digital sections.